Speak Rate Limits

Speak Rate Limits

Overview

Rate limit management for Speak's language learning API. Audio processing endpoints are computationally expensive, with stricter limits on pronunciation assessment than text-based endpoints.

Prerequisites

• Speak API access configured
• Understanding of audio processing latency
• Queue infrastructure for batch assessments

Speak API Rate Limits

Instructions

Step 1: Audio-Aware Rate Limiter

Audio endpoints are slower and more resource-intensive. Track per-endpoint.

import time, threading

class SpeakRateLimiter:
    def __init__(self):
        self.limits = {
            "pronunciation": 30,
            "conversation_start": 20,
            "conversation_turn": 60,
            "translation": 120,
        }
        self.windows = {k: [] for k in self.limits}
        self.lock = threading.Lock()

    def wait_if_needed(self, endpoint: str):
        with self.lock:
            now = time.time()
            self.windows[endpoint] = [t for t in self.windows[endpoint] if now - t < 60]
            if len(self.windows[endpoint]) >= self.limits[endpoint]:
                sleep_time = 60 - (now - self.windows[endpoint][0])
                time.sleep(sleep_time + 0.1)
            self.windows[endpoint].append(time.time())

limiter = SpeakRateLimiter()

def assess_pronunciation(client, audio_path, text):
    limiter.wait_if_needed("pronunciation")
    return client.assess_pronunciation(audio_path, text)

Step 2: Batch Assessment Queue

Queue multiple student recordings and process within rate limits.

from queue import PriorityQueue
import threading

class AssessmentQueue:
    def __init__(self, client, limiter):
        self.queue = PriorityQueue()
        self.client = client
        self.limiter = limiter
        self.results = {}

    def submit(self, student_id: str, audio_path: str, text: str, priority: int = 5):
        self.queue.put((priority, student_id, audio_path, text))

    def process_all(self) -> dict:
        while not self.queue.empty():
            priority, student_id, audio_path, text = self.queue.get()
            self.limiter.wait_if_needed("pronunciation")
            try:
                result = self.client.assess_pronunciation(audio_path, text)
                self.results[student_id] = {"score": result["score"], "status": "ok"}
            except Exception as e:
                self.results[student_id] = {"error": str(e), "status": "failed"}
        return self.results

Step 3: Handle 429 with Retry-After

def speak_with_retry(fn, *args, max_retries=3):
    for attempt in range(max_retries):
        try:
            return fn(*args)
        except requests.HTTPError as e:
            if e.response.status_code == 429:  # HTTP 429 Too Many Requests
                wait = int(e.response.headers.get("Retry-After", 5))
                time.sleep(wait)
            else:
                raise
    raise Exception("Max retries exceeded")

Error Handling

Examples

Rate Status Check

status = {endpoint: {
    "used": len(limiter.windows[endpoint]),
    "limit": limiter.limits[endpoint],
    "available": limiter.limits[endpoint] - len(limiter.windows[endpoint])
} for endpoint in limiter.limits}

Resources

• Speak API Docs

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale